Building geochemically based quantitative analogies from soil classification systems using different compositional datasets

Comparison of Different Classification Techniques Using Different Datasets

In this paper different classification techniques of Data Mining are compared using diverse datasets from University of California, Irvine(UCI). Accuracy and time required for execution by each technique is observed. The Data Mining refers to extracting or mining knowledge from huge volume of data. Classification is an important data mining technique with broad applications. It classifies data ...

Comparative Analysis of Classification Algorithms on Different Datasets using WEKA

Data mining is the upcoming research area to solve various problems and classification is one of main problem in the field of data mining. In this paper, we use two classification algorithms J48 (which is java implementation of C4. 5 algorithm) and multilayer perceptron alias MLP (which is a modification of the standard linear perceptron) of the Weka interface. It can be used for testing severa...

Quantitative Study of Root Systems in Different Soil Types.

Classification of Small Datasets: Why Using Class-Based Weighting Measures?

Abstract. In text classification, providing an efficient classifier even if the number of documents involved in the learning step is small remains an important issue. In this paper we evaluate the performance of traditional classification methods to better evaluate their limitation in the learning phase when dealing with small amount of documents. We thus propose a new way for weighting feature...

Using Optimization-Based Classification Method for Massive Datasets

Optimization-based algorithms, such as Multi-Criteria Linear programming (MCLP), have shown their effectiveness in classification. Nevertheless, due to the limitation of computation power and memory, it is difficult to apply MCLP, or similar optimization methods, to huge datasets. As the size of today’s databases is continuously increasing, it is highly important that data mining algorithms are...